2023-10-20 09:06:35,409 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,409 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(32001, 128) (position_embeddings): Embedding(512, 128) (token_type_embeddings): Embedding(2, 128) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-1): 2 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=128, out_features=128, bias=True) (key): Linear(in_features=128, out_features=128, bias=True) (value): Linear(in_features=128, out_features=128, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=128, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=128, out_features=512, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=512, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=128, out_features=128, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=128, out_features=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-20 09:06:35,410 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,410 MultiCorpus: 6183 train + 680 dev + 2113 test sentences - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator 2023-10-20 09:06:35,410 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,410 Train: 6183 sentences 2023-10-20 09:06:35,410 (train_with_dev=False, train_with_test=False) 2023-10-20 09:06:35,410 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,410 Training Params: 2023-10-20 09:06:35,410 - learning_rate: "3e-05" 2023-10-20 09:06:35,410 - mini_batch_size: "4" 2023-10-20 09:06:35,410 - max_epochs: "10" 2023-10-20 09:06:35,410 - shuffle: "True" 2023-10-20 09:06:35,410 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,411 Plugins: 2023-10-20 09:06:35,411 - TensorboardLogger 2023-10-20 09:06:35,411 - LinearScheduler | warmup_fraction: '0.1' 2023-10-20 09:06:35,411 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,411 Final evaluation on model from best epoch (best-model.pt) 2023-10-20 09:06:35,411 - metric: "('micro avg', 'f1-score')" 2023-10-20 09:06:35,411 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,411 Computation: 2023-10-20 09:06:35,411 - compute on device: cuda:0 2023-10-20 09:06:35,411 - embedding storage: none 2023-10-20 09:06:35,411 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,411 Model training base path: "hmbench-topres19th/en-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-20 09:06:35,411 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,411 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:35,411 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-20 09:06:37,833 epoch 1 - iter 154/1546 - loss 3.28397095 - time (sec): 2.42 - samples/sec: 5244.37 - lr: 0.000003 - momentum: 0.000000 2023-10-20 09:06:40,154 epoch 1 - iter 308/1546 - loss 2.99105285 - time (sec): 4.74 - samples/sec: 5139.46 - lr: 0.000006 - momentum: 0.000000 2023-10-20 09:06:42,531 epoch 1 - iter 462/1546 - loss 2.52293414 - time (sec): 7.12 - samples/sec: 5133.11 - lr: 0.000009 - momentum: 0.000000 2023-10-20 09:06:44,753 epoch 1 - iter 616/1546 - loss 2.02560749 - time (sec): 9.34 - samples/sec: 5282.45 - lr: 0.000012 - momentum: 0.000000 2023-10-20 09:06:46,974 epoch 1 - iter 770/1546 - loss 1.67950423 - time (sec): 11.56 - samples/sec: 5315.86 - lr: 0.000015 - momentum: 0.000000 2023-10-20 09:06:49,149 epoch 1 - iter 924/1546 - loss 1.45624081 - time (sec): 13.74 - samples/sec: 5319.42 - lr: 0.000018 - momentum: 0.000000 2023-10-20 09:06:51,518 epoch 1 - iter 1078/1546 - loss 1.28121677 - time (sec): 16.11 - samples/sec: 5324.77 - lr: 0.000021 - momentum: 0.000000 2023-10-20 09:06:53,979 epoch 1 - iter 1232/1546 - loss 1.14682597 - time (sec): 18.57 - samples/sec: 5319.74 - lr: 0.000024 - momentum: 0.000000 2023-10-20 09:06:56,359 epoch 1 - iter 1386/1546 - loss 1.05075802 - time (sec): 20.95 - samples/sec: 5283.56 - lr: 0.000027 - momentum: 0.000000 2023-10-20 09:06:58,781 epoch 1 - iter 1540/1546 - loss 0.96746300 - time (sec): 23.37 - samples/sec: 5294.97 - lr: 0.000030 - momentum: 0.000000 2023-10-20 09:06:58,888 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:06:58,888 EPOCH 1 done: loss 0.9635 - lr: 0.000030 2023-10-20 09:06:59,562 DEV : loss 0.1460493505001068 - f1-score (micro avg) 0.0 2023-10-20 09:06:59,573 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:07:01,954 epoch 2 - iter 154/1546 - loss 0.23058634 - time (sec): 2.38 - samples/sec: 5219.28 - lr: 0.000030 - momentum: 0.000000 2023-10-20 09:07:04,312 epoch 2 - iter 308/1546 - loss 0.22254356 - time (sec): 4.74 - samples/sec: 5059.52 - lr: 0.000029 - momentum: 0.000000 2023-10-20 09:07:06,666 epoch 2 - iter 462/1546 - loss 0.22366210 - time (sec): 7.09 - samples/sec: 4999.49 - lr: 0.000029 - momentum: 0.000000 2023-10-20 09:07:09,084 epoch 2 - iter 616/1546 - loss 0.21092520 - time (sec): 9.51 - samples/sec: 5084.24 - lr: 0.000029 - momentum: 0.000000 2023-10-20 09:07:11,446 epoch 2 - iter 770/1546 - loss 0.20806216 - time (sec): 11.87 - samples/sec: 5152.85 - lr: 0.000028 - momentum: 0.000000 2023-10-20 09:07:13,762 epoch 2 - iter 924/1546 - loss 0.20526902 - time (sec): 14.19 - samples/sec: 5138.44 - lr: 0.000028 - momentum: 0.000000 2023-10-20 09:07:16,170 epoch 2 - iter 1078/1546 - loss 0.20379508 - time (sec): 16.60 - samples/sec: 5111.99 - lr: 0.000028 - momentum: 0.000000 2023-10-20 09:07:18,549 epoch 2 - iter 1232/1546 - loss 0.20172155 - time (sec): 18.98 - samples/sec: 5138.82 - lr: 0.000027 - momentum: 0.000000 2023-10-20 09:07:20,996 epoch 2 - iter 1386/1546 - loss 0.20086049 - time (sec): 21.42 - samples/sec: 5126.79 - lr: 0.000027 - momentum: 0.000000 2023-10-20 09:07:23,418 epoch 2 - iter 1540/1546 - loss 0.19704649 - time (sec): 23.84 - samples/sec: 5183.51 - lr: 0.000027 - momentum: 0.000000 2023-10-20 09:07:23,513 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:07:23,513 EPOCH 2 done: loss 0.1964 - lr: 0.000027 2023-10-20 09:07:24,832 DEV : loss 0.09642348438501358 - f1-score (micro avg) 0.452 2023-10-20 09:07:24,844 saving best model 2023-10-20 09:07:24,878 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:07:27,163 epoch 3 - iter 154/1546 - loss 0.16756513 - time (sec): 2.28 - samples/sec: 5008.14 - lr: 0.000026 - momentum: 0.000000 2023-10-20 09:07:29,473 epoch 3 - iter 308/1546 - loss 0.15306420 - time (sec): 4.59 - samples/sec: 5243.02 - lr: 0.000026 - momentum: 0.000000 2023-10-20 09:07:31,759 epoch 3 - iter 462/1546 - loss 0.14828552 - time (sec): 6.88 - samples/sec: 5298.73 - lr: 0.000026 - momentum: 0.000000 2023-10-20 09:07:34,118 epoch 3 - iter 616/1546 - loss 0.15847543 - time (sec): 9.24 - samples/sec: 5349.69 - lr: 0.000025 - momentum: 0.000000 2023-10-20 09:07:36,476 epoch 3 - iter 770/1546 - loss 0.15836327 - time (sec): 11.60 - samples/sec: 5290.33 - lr: 0.000025 - momentum: 0.000000 2023-10-20 09:07:38,811 epoch 3 - iter 924/1546 - loss 0.15990269 - time (sec): 13.93 - samples/sec: 5374.68 - lr: 0.000025 - momentum: 0.000000 2023-10-20 09:07:41,185 epoch 3 - iter 1078/1546 - loss 0.16137110 - time (sec): 16.31 - samples/sec: 5359.58 - lr: 0.000024 - momentum: 0.000000 2023-10-20 09:07:43,553 epoch 3 - iter 1232/1546 - loss 0.16066108 - time (sec): 18.67 - samples/sec: 5353.33 - lr: 0.000024 - momentum: 0.000000 2023-10-20 09:07:45,922 epoch 3 - iter 1386/1546 - loss 0.16038985 - time (sec): 21.04 - samples/sec: 5281.58 - lr: 0.000024 - momentum: 0.000000 2023-10-20 09:07:48,314 epoch 3 - iter 1540/1546 - loss 0.15944440 - time (sec): 23.44 - samples/sec: 5277.46 - lr: 0.000023 - momentum: 0.000000 2023-10-20 09:07:48,405 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:07:48,405 EPOCH 3 done: loss 0.1593 - lr: 0.000023 2023-10-20 09:07:49,466 DEV : loss 0.0896943062543869 - f1-score (micro avg) 0.5099 2023-10-20 09:07:49,477 saving best model 2023-10-20 09:07:49,512 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:07:51,999 epoch 4 - iter 154/1546 - loss 0.15769143 - time (sec): 2.49 - samples/sec: 5070.16 - lr: 0.000023 - momentum: 0.000000 2023-10-20 09:07:54,306 epoch 4 - iter 308/1546 - loss 0.14433493 - time (sec): 4.79 - samples/sec: 5182.99 - lr: 0.000023 - momentum: 0.000000 2023-10-20 09:07:56,624 epoch 4 - iter 462/1546 - loss 0.15210379 - time (sec): 7.11 - samples/sec: 5030.59 - lr: 0.000022 - momentum: 0.000000 2023-10-20 09:07:58,995 epoch 4 - iter 616/1546 - loss 0.15295122 - time (sec): 9.48 - samples/sec: 5109.69 - lr: 0.000022 - momentum: 0.000000 2023-10-20 09:08:01,502 epoch 4 - iter 770/1546 - loss 0.15434920 - time (sec): 11.99 - samples/sec: 5061.65 - lr: 0.000022 - momentum: 0.000000 2023-10-20 09:08:03,980 epoch 4 - iter 924/1546 - loss 0.15086170 - time (sec): 14.47 - samples/sec: 5039.38 - lr: 0.000021 - momentum: 0.000000 2023-10-20 09:08:06,343 epoch 4 - iter 1078/1546 - loss 0.14718520 - time (sec): 16.83 - samples/sec: 5118.47 - lr: 0.000021 - momentum: 0.000000 2023-10-20 09:08:08,710 epoch 4 - iter 1232/1546 - loss 0.14718548 - time (sec): 19.20 - samples/sec: 5163.73 - lr: 0.000021 - momentum: 0.000000 2023-10-20 09:08:11,129 epoch 4 - iter 1386/1546 - loss 0.14835468 - time (sec): 21.62 - samples/sec: 5143.52 - lr: 0.000020 - momentum: 0.000000 2023-10-20 09:08:13,586 epoch 4 - iter 1540/1546 - loss 0.14859824 - time (sec): 24.07 - samples/sec: 5144.69 - lr: 0.000020 - momentum: 0.000000 2023-10-20 09:08:13,668 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:08:13,669 EPOCH 4 done: loss 0.1485 - lr: 0.000020 2023-10-20 09:08:14,728 DEV : loss 0.08766192942857742 - f1-score (micro avg) 0.5747 2023-10-20 09:08:14,738 saving best model 2023-10-20 09:08:14,771 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:08:17,083 epoch 5 - iter 154/1546 - loss 0.12769709 - time (sec): 2.31 - samples/sec: 5337.69 - lr: 0.000020 - momentum: 0.000000 2023-10-20 09:08:19,442 epoch 5 - iter 308/1546 - loss 0.13094414 - time (sec): 4.67 - samples/sec: 5128.06 - lr: 0.000019 - momentum: 0.000000 2023-10-20 09:08:21,893 epoch 5 - iter 462/1546 - loss 0.13300073 - time (sec): 7.12 - samples/sec: 5051.31 - lr: 0.000019 - momentum: 0.000000 2023-10-20 09:08:24,311 epoch 5 - iter 616/1546 - loss 0.13238196 - time (sec): 9.54 - samples/sec: 5160.07 - lr: 0.000019 - momentum: 0.000000 2023-10-20 09:08:26,690 epoch 5 - iter 770/1546 - loss 0.13428010 - time (sec): 11.92 - samples/sec: 5219.68 - lr: 0.000018 - momentum: 0.000000 2023-10-20 09:08:29,059 epoch 5 - iter 924/1546 - loss 0.13243603 - time (sec): 14.29 - samples/sec: 5203.51 - lr: 0.000018 - momentum: 0.000000 2023-10-20 09:08:31,427 epoch 5 - iter 1078/1546 - loss 0.13095759 - time (sec): 16.66 - samples/sec: 5184.83 - lr: 0.000018 - momentum: 0.000000 2023-10-20 09:08:33,859 epoch 5 - iter 1232/1546 - loss 0.13464576 - time (sec): 19.09 - samples/sec: 5174.45 - lr: 0.000017 - momentum: 0.000000 2023-10-20 09:08:36,291 epoch 5 - iter 1386/1546 - loss 0.13608735 - time (sec): 21.52 - samples/sec: 5199.06 - lr: 0.000017 - momentum: 0.000000 2023-10-20 09:08:38,636 epoch 5 - iter 1540/1546 - loss 0.13658634 - time (sec): 23.86 - samples/sec: 5187.95 - lr: 0.000017 - momentum: 0.000000 2023-10-20 09:08:38,723 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:08:38,723 EPOCH 5 done: loss 0.1363 - lr: 0.000017 2023-10-20 09:08:39,806 DEV : loss 0.08561883121728897 - f1-score (micro avg) 0.588 2023-10-20 09:08:39,817 saving best model 2023-10-20 09:08:39,849 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:08:42,216 epoch 6 - iter 154/1546 - loss 0.10872815 - time (sec): 2.37 - samples/sec: 5045.83 - lr: 0.000016 - momentum: 0.000000 2023-10-20 09:08:44,590 epoch 6 - iter 308/1546 - loss 0.11960726 - time (sec): 4.74 - samples/sec: 5003.13 - lr: 0.000016 - momentum: 0.000000 2023-10-20 09:08:46,973 epoch 6 - iter 462/1546 - loss 0.13441599 - time (sec): 7.12 - samples/sec: 5024.85 - lr: 0.000016 - momentum: 0.000000 2023-10-20 09:08:49,131 epoch 6 - iter 616/1546 - loss 0.13530848 - time (sec): 9.28 - samples/sec: 5247.57 - lr: 0.000015 - momentum: 0.000000 2023-10-20 09:08:51,443 epoch 6 - iter 770/1546 - loss 0.14041532 - time (sec): 11.59 - samples/sec: 5202.06 - lr: 0.000015 - momentum: 0.000000 2023-10-20 09:08:53,814 epoch 6 - iter 924/1546 - loss 0.13528183 - time (sec): 13.96 - samples/sec: 5249.94 - lr: 0.000015 - momentum: 0.000000 2023-10-20 09:08:56,177 epoch 6 - iter 1078/1546 - loss 0.13227395 - time (sec): 16.33 - samples/sec: 5253.32 - lr: 0.000014 - momentum: 0.000000 2023-10-20 09:08:58,522 epoch 6 - iter 1232/1546 - loss 0.13103216 - time (sec): 18.67 - samples/sec: 5297.52 - lr: 0.000014 - momentum: 0.000000 2023-10-20 09:09:00,904 epoch 6 - iter 1386/1546 - loss 0.12991021 - time (sec): 21.05 - samples/sec: 5249.58 - lr: 0.000014 - momentum: 0.000000 2023-10-20 09:09:03,307 epoch 6 - iter 1540/1546 - loss 0.13230097 - time (sec): 23.46 - samples/sec: 5276.90 - lr: 0.000013 - momentum: 0.000000 2023-10-20 09:09:03,406 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:09:03,406 EPOCH 6 done: loss 0.1321 - lr: 0.000013 2023-10-20 09:09:04,491 DEV : loss 0.08819162845611572 - f1-score (micro avg) 0.6039 2023-10-20 09:09:04,502 saving best model 2023-10-20 09:09:04,536 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:09:07,006 epoch 7 - iter 154/1546 - loss 0.11723149 - time (sec): 2.47 - samples/sec: 5437.44 - lr: 0.000013 - momentum: 0.000000 2023-10-20 09:09:09,369 epoch 7 - iter 308/1546 - loss 0.11870071 - time (sec): 4.83 - samples/sec: 5146.87 - lr: 0.000013 - momentum: 0.000000 2023-10-20 09:09:11,751 epoch 7 - iter 462/1546 - loss 0.11534894 - time (sec): 7.21 - samples/sec: 5251.42 - lr: 0.000012 - momentum: 0.000000 2023-10-20 09:09:14,090 epoch 7 - iter 616/1546 - loss 0.12686834 - time (sec): 9.55 - samples/sec: 5179.35 - lr: 0.000012 - momentum: 0.000000 2023-10-20 09:09:16,467 epoch 7 - iter 770/1546 - loss 0.12653362 - time (sec): 11.93 - samples/sec: 5208.68 - lr: 0.000012 - momentum: 0.000000 2023-10-20 09:09:18,835 epoch 7 - iter 924/1546 - loss 0.12736348 - time (sec): 14.30 - samples/sec: 5228.86 - lr: 0.000011 - momentum: 0.000000 2023-10-20 09:09:21,220 epoch 7 - iter 1078/1546 - loss 0.12994923 - time (sec): 16.68 - samples/sec: 5236.09 - lr: 0.000011 - momentum: 0.000000 2023-10-20 09:09:23,602 epoch 7 - iter 1232/1546 - loss 0.12775059 - time (sec): 19.06 - samples/sec: 5245.07 - lr: 0.000011 - momentum: 0.000000 2023-10-20 09:09:26,092 epoch 7 - iter 1386/1546 - loss 0.12761801 - time (sec): 21.56 - samples/sec: 5181.77 - lr: 0.000010 - momentum: 0.000000 2023-10-20 09:09:28,442 epoch 7 - iter 1540/1546 - loss 0.12680413 - time (sec): 23.91 - samples/sec: 5179.14 - lr: 0.000010 - momentum: 0.000000 2023-10-20 09:09:28,532 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:09:28,532 EPOCH 7 done: loss 0.1266 - lr: 0.000010 2023-10-20 09:09:29,594 DEV : loss 0.08853663504123688 - f1-score (micro avg) 0.5949 2023-10-20 09:09:29,605 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:09:31,902 epoch 8 - iter 154/1546 - loss 0.10560379 - time (sec): 2.30 - samples/sec: 5298.60 - lr: 0.000010 - momentum: 0.000000 2023-10-20 09:09:34,349 epoch 8 - iter 308/1546 - loss 0.12872546 - time (sec): 4.74 - samples/sec: 5252.11 - lr: 0.000009 - momentum: 0.000000 2023-10-20 09:09:36,720 epoch 8 - iter 462/1546 - loss 0.13119470 - time (sec): 7.11 - samples/sec: 5187.26 - lr: 0.000009 - momentum: 0.000000 2023-10-20 09:09:39,051 epoch 8 - iter 616/1546 - loss 0.12502116 - time (sec): 9.45 - samples/sec: 5215.10 - lr: 0.000009 - momentum: 0.000000 2023-10-20 09:09:41,413 epoch 8 - iter 770/1546 - loss 0.12112850 - time (sec): 11.81 - samples/sec: 5278.34 - lr: 0.000008 - momentum: 0.000000 2023-10-20 09:09:43,859 epoch 8 - iter 924/1546 - loss 0.12463981 - time (sec): 14.25 - samples/sec: 5309.41 - lr: 0.000008 - momentum: 0.000000 2023-10-20 09:09:46,162 epoch 8 - iter 1078/1546 - loss 0.12423906 - time (sec): 16.56 - samples/sec: 5263.09 - lr: 0.000008 - momentum: 0.000000 2023-10-20 09:09:48,482 epoch 8 - iter 1232/1546 - loss 0.12613505 - time (sec): 18.88 - samples/sec: 5225.70 - lr: 0.000007 - momentum: 0.000000 2023-10-20 09:09:50,918 epoch 8 - iter 1386/1546 - loss 0.12382574 - time (sec): 21.31 - samples/sec: 5199.99 - lr: 0.000007 - momentum: 0.000000 2023-10-20 09:09:53,355 epoch 8 - iter 1540/1546 - loss 0.12351904 - time (sec): 23.75 - samples/sec: 5219.53 - lr: 0.000007 - momentum: 0.000000 2023-10-20 09:09:53,443 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:09:53,443 EPOCH 8 done: loss 0.1233 - lr: 0.000007 2023-10-20 09:09:54,533 DEV : loss 0.09046540409326553 - f1-score (micro avg) 0.6062 2023-10-20 09:09:54,545 saving best model 2023-10-20 09:09:54,583 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:09:56,973 epoch 9 - iter 154/1546 - loss 0.12165880 - time (sec): 2.39 - samples/sec: 5120.10 - lr: 0.000006 - momentum: 0.000000 2023-10-20 09:09:59,354 epoch 9 - iter 308/1546 - loss 0.11720672 - time (sec): 4.77 - samples/sec: 5174.59 - lr: 0.000006 - momentum: 0.000000 2023-10-20 09:10:01,652 epoch 9 - iter 462/1546 - loss 0.10950130 - time (sec): 7.07 - samples/sec: 5397.14 - lr: 0.000006 - momentum: 0.000000 2023-10-20 09:10:03,813 epoch 9 - iter 616/1546 - loss 0.11189291 - time (sec): 9.23 - samples/sec: 5423.30 - lr: 0.000005 - momentum: 0.000000 2023-10-20 09:10:05,958 epoch 9 - iter 770/1546 - loss 0.11566478 - time (sec): 11.37 - samples/sec: 5567.08 - lr: 0.000005 - momentum: 0.000000 2023-10-20 09:10:08,178 epoch 9 - iter 924/1546 - loss 0.12061263 - time (sec): 13.59 - samples/sec: 5535.08 - lr: 0.000005 - momentum: 0.000000 2023-10-20 09:10:10,589 epoch 9 - iter 1078/1546 - loss 0.12124174 - time (sec): 16.01 - samples/sec: 5494.20 - lr: 0.000004 - momentum: 0.000000 2023-10-20 09:10:12,970 epoch 9 - iter 1232/1546 - loss 0.12235867 - time (sec): 18.39 - samples/sec: 5445.86 - lr: 0.000004 - momentum: 0.000000 2023-10-20 09:10:15,300 epoch 9 - iter 1386/1546 - loss 0.12037485 - time (sec): 20.72 - samples/sec: 5384.65 - lr: 0.000004 - momentum: 0.000000 2023-10-20 09:10:17,700 epoch 9 - iter 1540/1546 - loss 0.11940803 - time (sec): 23.12 - samples/sec: 5359.10 - lr: 0.000003 - momentum: 0.000000 2023-10-20 09:10:17,787 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:10:17,787 EPOCH 9 done: loss 0.1193 - lr: 0.000003 2023-10-20 09:10:18,860 DEV : loss 0.09189001470804214 - f1-score (micro avg) 0.6117 2023-10-20 09:10:18,871 saving best model 2023-10-20 09:10:18,902 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:10:21,118 epoch 10 - iter 154/1546 - loss 0.12687807 - time (sec): 2.22 - samples/sec: 5422.87 - lr: 0.000003 - momentum: 0.000000 2023-10-20 09:10:23,371 epoch 10 - iter 308/1546 - loss 0.12068074 - time (sec): 4.47 - samples/sec: 5568.10 - lr: 0.000003 - momentum: 0.000000 2023-10-20 09:10:25,575 epoch 10 - iter 462/1546 - loss 0.12030419 - time (sec): 6.67 - samples/sec: 5713.42 - lr: 0.000002 - momentum: 0.000000 2023-10-20 09:10:27,713 epoch 10 - iter 616/1546 - loss 0.11605372 - time (sec): 8.81 - samples/sec: 5773.02 - lr: 0.000002 - momentum: 0.000000 2023-10-20 09:10:30,067 epoch 10 - iter 770/1546 - loss 0.11801476 - time (sec): 11.16 - samples/sec: 5649.92 - lr: 0.000002 - momentum: 0.000000 2023-10-20 09:10:32,425 epoch 10 - iter 924/1546 - loss 0.11442404 - time (sec): 13.52 - samples/sec: 5565.71 - lr: 0.000001 - momentum: 0.000000 2023-10-20 09:10:34,785 epoch 10 - iter 1078/1546 - loss 0.11142937 - time (sec): 15.88 - samples/sec: 5542.35 - lr: 0.000001 - momentum: 0.000000 2023-10-20 09:10:37,138 epoch 10 - iter 1232/1546 - loss 0.11098365 - time (sec): 18.24 - samples/sec: 5458.00 - lr: 0.000001 - momentum: 0.000000 2023-10-20 09:10:39,490 epoch 10 - iter 1386/1546 - loss 0.11535574 - time (sec): 20.59 - samples/sec: 5430.96 - lr: 0.000000 - momentum: 0.000000 2023-10-20 09:10:41,875 epoch 10 - iter 1540/1546 - loss 0.11683715 - time (sec): 22.97 - samples/sec: 5397.34 - lr: 0.000000 - momentum: 0.000000 2023-10-20 09:10:41,963 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:10:41,963 EPOCH 10 done: loss 0.1167 - lr: 0.000000 2023-10-20 09:10:43,032 DEV : loss 0.0931963250041008 - f1-score (micro avg) 0.6154 2023-10-20 09:10:43,043 saving best model 2023-10-20 09:10:43,106 ---------------------------------------------------------------------------------------------------- 2023-10-20 09:10:43,106 Loading model from best epoch ... 2023-10-20 09:10:43,182 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET 2023-10-20 09:10:46,022 Results: - F-score (micro) 0.5552 - F-score (macro) 0.2272 - Accuracy 0.4001 By class: precision recall f1-score support LOC 0.6223 0.6321 0.6272 946 BUILDING 0.1333 0.0108 0.0200 185 STREET 0.5000 0.0179 0.0345 56 micro avg 0.6145 0.5063 0.5552 1187 macro avg 0.4185 0.2203 0.2272 1187 weighted avg 0.5403 0.5063 0.5046 1187 2023-10-20 09:10:46,022 ----------------------------------------------------------------------------------------------------